4 papers
Almost Sure Convergence of Differential Temporal Difference Learning for Average Reward Markov Decision Processes
Ethan Blaser, Jiuqi Wang, Shangtong Zhang
The average reward is a fundamental performance metric in reinforcement learning (RL) focusing on the long-run performance of an agent. Differential temporal difference (TD) learni…
A Survey of In-Context Reinforcement Learning
Amir Moeini, Jiuqi Wang, Jacob Beck +4
Reinforcement learning (RL) agents typically optimize their policies by performing expensive backward passes to update their network parameters. However, some agents can solve new…
Asymptotic and Finite Sample Analysis of Nonexpansive Stochastic Approximations with Markovian Noise
Ethan Blaser, Shangtong Zhang
Stochastic approximation is a powerful class of algorithms with celebrated success. However, a large body of previous analysis focuses on stochastic approximations driven by contra…
Transformers Can Learn Temporal Difference Methods for In-Context Reinforcement Learning
Jiuqi Wang, Ethan Blaser, Hadi Daneshmand +1
Traditionally, reinforcement learning (RL) agents learn to solve new tasks by updating their neural network parameters through interactions with the task environment. However, rece…